Publications related to Stable Myoelectric Control of a Hand Prosthesis using Non-Linear Incremental Learning

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Patrick Thiran, Negar Kiyavash, Saber Salehkaleybar

We study the performance of Stochastic Cubic Regularized Newton (SCRN) on a class of functions satisfying gradient dominance property with

1\le\alpha\le2

which holds in a wide range of applications in machine learning and signal processing. This conditio ...

NeurIPS2022

Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization

Volkan Cevher, Alp Yurtsever, Maria-Luiza Vladarean

We propose a stochastic conditional gradient method (CGM) for minimizing convex finitesum objectives formed as a sum of smooth and non-smooth terms. Existing CGM variants for this template either suffer from slow convergence rates, or require carefully inc ...

2022

Simultaneous Prediction of Wrist and Hand Motions via Wearable Ultrasound Sensing for Natural Control of Hand Prostheses

Xingchen Yang

Simultaneous prediction of wrist and hand motions is essential for the natural interaction with hand prostheses. In this paper, we propose a novel multi-out Gaussian process (MOGP) model and a multi-task deep learning (MTDL) algorithm to achieve simultaneo ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Toward higher-performance bionic limbs for wider clinical use

Silvestro Micera

Most prosthetic limbs can autonomously move with dexterity, yet they are not perceived by the user as belonging to their own body. Robotic limbs can convey information about the environment with higher precision than biological limbs, but their actual perf ...

NATURE RESEARCH2021

Cognitive workload monitoring in virtual reality based rescue missions with drones

David Atienza Alonso, Adriana Arza Valdes, Fabio Isidoro Tiberio Dell'Agnola, Niloofar Momeni

The use of drones in search and rescue (SAR) missions can be very cognitively demanding. Since high levels of cognitive workload can negatively affect human performance, there is a risk of compromising the mission and leading to failure with catastrophic o ...

2020

Learning Positive Functions with Pseudo Mirror Descent

Negar Kiyavash

The nonparametric learning of positive-valued functions appears widely in machine learning, especially in the context of estimating intensity functions of point processes. Yet, existing approaches either require computing expensive projections or semidefin ...

Curran Associates, Inc.2019

Development of a simulated transtibial amputee model

Auke Ijspeert, Florin Dzeladini, César José Cardoso Ferreira

Currently, there are more than 30 million amputees in the world and each year thousands of people suffer from amputation and, therefore, the development of lower limb prostheses is crucial to improve the quality of millions of people's lives by restoring t ...

IEEE2019

Online Adaptive Methods, Universality and Acceleration

Volkan Cevher, Alp Yurtsever

We present a novel method for convex unconstrained optimization that, without any modifications, ensures: (i) accelerated convergence rate for smooth objectives, (ii) standard convergence rate in the general (non-smooth) setting, and (iii) standard converg ...

2018

A Physically-Consistent Bayesian Non-Parametric Mixture Model for Dynamical System Learning

Aude Billard, Nadia Barbara Figueroa Fernandez

We propose a physically-consistent Bayesian non-parametric approach for fitting Gaussian Mixture Models (GMM) on trajectory data. Physical-consistency of the GMM is ensured by imposing a prior on the component assignments biased by a novel similarity metri ...

2018

The brain strategy for online learning

Ali H. Sayed, Stefan Vlaski, Bicheng Ying

Complexity is a double-edged sword for learning algorithms when the number of available samples for training in relation to the dimension of the feature space is small. This is because simple models do not sufficiently capture the nuances of the data set, ...

IEEE2016

Stable Myoelectric Control of a Hand Prosthesis using Non-Linear Incremental Learning

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Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization

Simultaneous Prediction of Wrist and Hand Motions via Wearable Ultrasound Sensing for Natural Control of Hand Prostheses

Toward higher-performance bionic limbs for wider clinical use

Cognitive workload monitoring in virtual reality based rescue missions with drones

Learning Positive Functions with Pseudo Mirror Descent

Development of a simulated transtibial amputee model

Online Adaptive Methods, Universality and Acceleration

A Physically-Consistent Bayesian Non-Parametric Mixture Model for Dynamical System Learning

The brain strategy for online learning

Simultaneous Prediction of Wrist and Hand Motions via Wearable Ultrasound Sensing for Natural Control of Hand Prostheses

Cognitive workload monitoring in virtual reality based rescue missions with drones

Learning Positive Functions with Pseudo Mirror Descent

A Physically-Consistent Bayesian Non-Parametric Mixture Model for Dynamical System Learning

The brain strategy for online learning

Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Online Adaptive Methods, Universality and Acceleration

Toward higher-performance bionic limbs for wider clinical use

Development of a simulated transtibial amputee model